\(\alpha \)  was set to 0.98, in order to have the 2 terms of the loss at the same scale. No grid search was carried over this term.
\(MSSIM\)  is the multiscale structural similarity index defined in [1]} with the default TensorFlow parametershttps://www.tensorflow.org/api_docs/python/tf/image/ssim_multiscale.
We used the RADAM optimizer [2]} with a learning rate of \(10^{-4}\)  and the default TensorFlow parametershttps://www.tensorflow.org/addons/api_docs/python/tfa/optimizers.
To allow for an easier training, we scale the data by \(10^6\)  in order to avoid low values that result in unstable training. No grid search was carried over this term.
Each batch is composed of exactly one slice of a whole volume taken at randomContrarily to our previous work we do not select only central slices.. An epoch is defined as seeing exactly one slice of each volume in the training data set, that is 973 slices.
